Accuracy Assurance Methods

Calibration and validation are key concepts in analytical chemistry, quality control, and instrument operation. They ensure the accuracy, precision, and reliability of measurements and methods. Although they are closely related, they have distinct purposes and processes. Calibration: Calibration is the process of setting and verifying the accuracy of an instrument or method by comparing its response to a known standard or reference. Purpose: To ensure the instrument provides accurate and reliable results. Key Features: Establishes a relationship between the instrument’s response and the known values of a standard. Performed periodically to maintain accuracy over time. Corrects for systematic errors. Process: Use reference standards with known concentrations or values. Measure the response of the instrument. Plot the instrument's response against the known values (e.g., a calibration curve). Adjust the instrument if needed. Example Applications: Calibrating a pH meter with buffer solutions. Generating a calibration curve in UV-Vis spectroscopy for concentration determination. Validation: Validation is the process of proving that a method, process, or instrument consistently produces results that meet predetermined criteria. Purpose: To ensure the reliability, accuracy, precision, and suitability of a method or instrument for its intended use. Key Features: Focuses on the performance of the entire method or system, not just the instrument. Usually performed before using a method or system routinely. Involves systematic testing of parameters such as accuracy, precision, sensitivity, and specificity. Process: Define validation criteria (e.g., limits for accuracy, precision). Perform tests to evaluate each parameter. Document results and assess compliance with criteria. Approve the method or system if it meets all validation criteria. Example Applications: Validating an HPLC method for drug analysis in pharmaceutical applications. Validating a sterilization process in medical device manufacturing.

Method validation and verification are essential processes in laboratory settings to ensure the accuracy, reliability, and consistency of analytical results. *Method Validation:* Method validation involves systematically evaluating and documenting the performance characteristics of an analytical method to ensure it meets the intended use. Key aspects of method validation: 1. Specificity 2. Sensitivity 3. Accuracy 4. Precision (Repeatability, Reproducibility) 5. Linearity 6. Range 7. Limit of Detection (LOD) 8. Limit of Quantitation (LOQ) 9. Robustness *Method Verification:* Method verification confirms that a validated method performs as expected in a specific laboratory setting. Key aspects of method verification: 1. Verification of method performance characteristics 2. Verification of instrument calibration 3. Verification of analyst competence 4. Comparison with certified reference materials 5. Evaluation of method uncertainty *Steps in Method Validation and Verification:* 1. Pre-validation/verification (review of method, instrumentation, and reagents) 2. Development and optimization of the method 3. Validation/verification experiments (e.g., precision, accuracy, linearity) 4. Data analysis and interpretation 5. Documentation and reporting 6. Ongoing monitoring and maintenance *Regulatory Requirements:* 1. FDA (21 CFR Part 11, 21 CFR Part 58) 2. ISO/IEC 17025 3. EPA (40 CFR Part 136) 4. USP (United States Pharmacopeia) 5. ICH (International Council for Harmonisation) *Best Practices:* 1. Use standardized protocols and procedures 2. Document all validation/verification activities 3. Involve multiple analysts and instruments 4. Use certified reference materials 5. Regularly review and update validation/verification documents *Common Challenges:* 1. Lack of resources (time, personnel, equipment) 2. Insufficient method optimization 3. Inadequate training and analyst competence 4. Incomplete documentation 5. Failure to monitor and maintain method performance *Tools and Resources:* 1. Validation and verification protocols 2. Standard operating procedures (SOPs) 3. Analytical instrument manuals 4. Regulatory guidelines and standards 5. Statistical software and tools (e.g., Minitab, Excel) By following these guidelines and best practices, laboratories can ensure the accuracy, reliability, and consistency of their analytical results. Do you have any specific questions about method validation and verification?

🔬 How Can Quality Assurance Activities Enhance a Testing Laboratory's Performance? Quality assurance activities form the foundation of consistent and reliable results in testing laboratories. According to ISO/IEC 17025:2017, quality assurance measures help validate results and ensure continuous improvement. Internal Quality Assurance Activities: The following activities are pivotal in monitoring the validity of results: 1. Replicate Testing: Performing the same test multiple times under similar conditions to verify repeatability and identify potential variability. 2. Retesting of Retained Samples: Testing previously retained samples to confirm consistency in results over time or under different conditions. 3. Testing with Blind Samples: Using samples with unknown properties (to the tester) ensures objective evaluation and eliminates bias in results. 4. Use of Certified Reference Materials (CRMs): Regularly employing CRMs validates the accuracy of testing methods and aligns results with traceable international standards. 5. Control Charts and Trend Analysis: Continuously monitoring key performance metrics through statistical tools highlights deviations and helps maintain stability in test results. 6. Intermediate Checks on Equipment: Periodic checks ensure that instruments remain within calibration limits and deliver reliable measurements. 7. Result Correlation Across Methods or Equipment: Comparing outcomes from different methods or instruments ensures consistency and highlights potential discrepancies. 8. Split Sample Testing: Distributing parts of the same sample for testing under varying conditions or by different personnel to verify uniformity. 9. Analysis of Historical Data: Reviewing historical results to identify long-term patterns or anomalies that might require corrective action. External Quality Assurance Activities: To complement internal measures, laboratories are encouraged to: 1. Participate in Proficiency Testing (PT): Comparing results with peer laboratories to benchmark performance and identify improvement areas. 2. Engage in Inter-Laboratory Comparisons (ILC): Sharing and analyzing results with other laboratories to validate methods and maintain consistency. Why These Activities Matter? Enhanced Accuracy and Precision: Frequent validation ensures that results remain reliable and meet client expectations. Proactive Issue Resolution: Monitoring trends and performing checks early helps identify and address problems before they escalate. Reputation and Credibility: A robust quality assurance system demonstrates technical competence, building customer trust and competitive advantage. By integrating these activities, laboratories can ensure reliable performance and maintain compliance with ISO/IEC 17025:2017. 💡 What QA practices does your lab implement to uphold quality standards? Let’s discuss! #QualityAssurance #ISO17025 #LaboratoryExcellence #TestingReliability #ContinuousImprovement

The Critical Role of QAQC in Mineral Exploration and Mining: Ensuring Data Integrity and Project Success In the mineral exploration and mining industries, Quality Assurance and Quality Control (QAQC) are fundamental for ensuring reliable data, minimizing risks, and optimizing resource development. A robust QAQC system ensures standardized procedures throughout exploration—from sampling to analysis—enhancing decision-making and minimizing uncertainties. 1. Sample Collection & Handling Accurate exploration results depend on careful sampling protocols. Samples must be representative, collected properly, and preserved to avoid contamination. Chain-of-custody tracking ensures sample integrity from field collection to laboratory analysis. 2. Geological Logging Geological logging is essential for creating resource models. Accurate, consistent logging of lithology, mineralization, and alteration data is crucial for reliable resource estimation and modeling. 3. Laboratory Analysis Analytical methods must be precise and standardized. Laboratories should use accredited techniques (e.g., ICP, XRF, fire assays) and regularly calibrate equipment to ensure accurate assay results. 4. Certified Reference Materials (CRMs), Blanks, and Duplicates The use of CRMs, blanks, and duplicates in assay batches helps identify errors in the analysis, verify assay accuracy, detect contamination, and assess precision. 5. Geophysical & Geochemical Survey QAQC QAQC in geophysical and geochemical surveys ensures that instruments are calibrated correctly and consistent methods are used. Environmental corrections and quality control in geochemical sampling ensure the reliability of survey data. 6. Data Integrity and Management QAQC in data management is essential for maintaining the integrity of geological data. Proper validation checks allow for early detection of discrepancies, ensuring that only accurate, verifiable data is used in resource modeling. 7. Drill Core Sampling Drill core sampling protocols are key for obtaining representative samples. Duplicate sampling and assay validation help ensure that core results reflect the true characteristics of the deposit, supporting accurate resource estimation. 8. Statistical Analysis Geostatistical methods, like kriging and cross-validation, help evaluate assay data variability and optimize resource models by quantifying uncertainty and identifying anomalies. 9. Reporting Standards & Regulatory Compliance Adhering to international reporting standards such as JORC, NI 43-101, and SAMREC ensures transparency and consistency in resource reporting, providing confidence to stakeholders and investors. By embedding QAQC principles across all stages of exploration, companies can ensure more accurate resource estimations, reduce risks, and improve the efficiency and success of their mining operations. #QAQC #MineralExploration #Geology #GeochemicalAnalysis #GeologicalLogging #ResourceEstimation #GeologicalData

We follow standard methods, so verification is not needed. I’ve heard this from more than one lab team. But here’s what ISO/IEC 17025:2017 Clause 7.2 actually requires—and why just using a published method is not enough. Clause 7.2 – Selection, Verification, and Validation of Methods is clear in its intent: 1. Select the Right Method → Use validated methods—preferably international, national, or industry standards. → If you’re using a non-standard or lab-developed method, you need documented justification. 2. Verify Every Standard Method → Yes, even published ones. → Your lab must confirm that the method produces valid results in your environment, with your equipment, personnel, and conditions. → This is method verification—and it is mandatory. 3. Validate When Required → If the method is non-standard, modified, or developed internally, full method validation is required. → That means you must demonstrate performance characteristics like accuracy, precision, linearity, detection limits, and robustness. 4. Keep Clear Records → The process, data, and outcomes of both verification and validation must be documented. → This includes how you confirmed the method is fit for purpose. Practical Tips → Modifying a drying time due to local humidity? Validate the impact. → Using ISO 10523 for pH testing? Verify that your team and equipment consistently meet required performance. → Developing a new test? Validate before use—then document the whole process. ISO/IEC 17025 doesn’t just ask what method you use—it asks if you’ve proven it works for you. #ISO17025 #LaboratoryAccreditation #MethodValidation #Verification #QualityAssurance #LabManagement #TestingLabs #Clause7Explained #meghasoni

Quality Assurance (QA) and Quality Control (QC) are critical components of any mineral exploration project to ensure the reliability and accuracy of the data collected, which ultimately affects the interpretation and decision-making processes. Here's a breakdown of how QA and QC are typically applied in mineral exploration: #Quality #Assurance (QA) QA is the overarching process that ensures that all procedures and practices in the exploration project are carried out systematically and meet predetermined standards. It includes: 1. #Standard #Operating #Procedures (SOPs): Establishing and following SOPs for sampling, sample handling, logging, and assaying to minimize errors and biases. 2. #Training: Ensuring all team members are properly trained and competent in their roles to maintain consistency in data collection and processing. 3. #Documentation: Keeping detailed records of procedures, equipment calibration, and maintenance logs to ensure traceability and transparency. 4. #Sample #Security: Implementing measures to protect the integrity of samples, such as proper labeling, secure storage, and chain-of-custody protocols. 5. #Audits: Regular internal and external audits to verify that QA protocols are being followed and to identify areas for improvement. #Quality #Control (QC) QC involves the specific measures taken to monitor and verify the accuracy and precision of data collected during the exploration process. It includes: 1. #Blanks: Using blank samples to detect contamination during sample preparation and analysis. This is something you're already familiar with and use to quickly identify contamination issues. 2. #Standards (Certified Reference Materials): Inserting standards into the sample stream to assess the accuracy of the analytical methods and to detect any systematic errors. 3. #Duplicates: Analyzing duplicate samples to check the precision of sampling and analytical processes. This can include field duplicates, coarse duplicates, and pulp duplicates. 4. #Control #Charts: Plotting results of standards and blanks on control charts to visually monitor data quality over time and quickly identify any deviations or trends. 5. #Data #Verification: Regularly reviewing and verifying data for any inconsistencies, outliers, or errors. This can include re-assaying or re-sampling in case of suspicious results. 6. #Cross-Lab #Checks: Sending a subset of samples to a secondary laboratory to verify the results from the primary lab, ensuring that the data is consistent and reliable. #Application #in #Exploration** - #Geochemical #Sampling: Implementing QC procedures in soil, rock, and stream sediment sampling to ensure representativeness and reliability of the geochemical data. - #Drilling #Programs: Incorporating QA/QC in core logging, sample splitting, and assaying to maintain the integrity of the geological data. - #Resource #Estimation: Using variograms and other geostatistical tools to evaluate the spatial variability. https://t.me/OreZone

"Limited vs. Reasonable Assurance: A Deep Dive into the Procedures" As ESG reporting faces increasing scrutiny, assurance has become a crucial tool for enhancing trust and transparency. But what exactly happens in an assurance engagement? Let’s break down the procedures for Limited and Reasonable Assurance and why they matter. Limited Assurance: Limited assurance is less intensive, relying primarily on analytical procedures and inquiries rather than extensive testing. The assurance provider gives a negative assurance statement, meaning they report if anything appears materially misstated—but they do not explicitly confirm accuracy. Key procedures in Limited Assurance: - Inquiries: Engaging with management and staff to understand reporting processes. - Analytical procedures: Comparing ESG data over time or benchmarking against industry standards. - Review of documentation: Examining sustainability reports, policies, and select supporting evidence. - Sampling (if any) is limited: Small-scale checks rather than large-scale verification. - Site visits (if any) are minimal: Typically, virtual walkthroughs or selective on-site observations. - No deep testing of controls or data verification – The process assesses plausibility rather than verifying accuracy in depth. Outcome in the assurance report – "Nothing has come to our attention that suggests the information is materially misstated." Reasonable Assurance: Reasonable assurance is far more detailed, akin to financial audits. The assurance provider actively tests ESG disclosures and issues a positive assurance statement, meaning they confirm accuracy to the best of their knowledge. Key procedures in Reasonable Assurance: - Detailed inquiries: In-depth discussions with senior management and process owners. - Substantive testing: Verification of ESG data with source documents. - Controls testing: Evaluating internal controls, data collection systems, and governance frameworks. - Site visits & physical inspection – Visiting facilities, verifying environmental impact measures, and checking labor conditions. - Expanded sampling: Examining a broader dataset for inconsistencies or errors. Outcome in the assurance report – "In our opinion, the ESG disclosures are free from material misstatement and present a true and fair view." As ESG reporting evolves, the shift from Limited to Reasonable Assurance is inevitable. Companies that invest in robust data governance, internal controls, and verification mechanisms today will be best positioned for the future. Assurance isn’t just about compliance—it’s about building trust. ESG Briefings by Lodha & Co LLP #assurance #limitedassurance #reasonableassurance #esg #sustainability #verification #validation #corporategovernance